基于机器视觉技术的烤烟鲜烟叶成熟度检测

为准确判定烟叶采收成熟度,以不同成熟度中部烟叶为材料,利用机器视觉技术提取不同成熟度烟叶图像的颜色和纹理特征值,采用主成分分析法对3个颜色特征值(色调、饱和度、亮度)和5个纹理特征值(角二阶矩、相关度、熵、对比度、逆差距)进行优化,利用BP神经网络建立烟叶成熟度检测模型。结果表明,采用前4个主成分可综合反映3个颜色特征值和5个纹理特征值的分级信息,实现了参数的优化;在图像信息主成分因子数为4,中间节点数为16时,该识别模型最佳,模型平均识别率为93.67%,表明基于机器视觉技术对烤烟鲜烟叶成熟度的检测是可行的。

Determination of the maturity grades of fresh leaves for flue-cured tobacco

In order to realize accurate and objective determination of the maturity grades of tobacco leaves.Middle part of tobacco leaves with different grades of maturity was used to extract color and vein characteristics with machine vision technique.Three color characteristics(H,S,V) and five vein characteristics(energy,correlation degree,entropy,contrast,inverse difference moment) were optimized by principal components analysis.Maturity grading models were built by back-propagation(BP)neural network.The result showed that the first four principal components together could represent the information of the three color characteristics and the five vein characteristics needed for grading,which realized the optimization of parameters.When the number of principal component factor was 4 and the number of nodes of hidden layer was 16,this grading model showed the best performance with average recognition rate of 93.67%.The overall results show that it is feasible to discriminate the maturity grades of fresh tobacco leaves with machine vision technique.